Machine Learning–Driven Prediction of Asthma Exacerbations: A Step Forward Toward Personalized Care — A Narrative Review

Abstract

Asthma exacerbations present a major difficulty in asthma management, necessitating the development of effective strategies for timely intervention and prevention. This study examined recent advances and knowledge regarding using machine learning (ML) methods to predict asthma exacerbation in different populations. Through a thorough analysis of existing literature, the study investigates various ML methods, including specific risk factors and biomarkers, and their potential impact on clinical practice. Recent studies have shown that ML can effectively predict exacerbation by utilizing patient demographics, clinical characteristics, environmental factors, and biomarkers. Innovative approaches for extracting distinctive features, ensemble learning techniques, and personalized, predictive models have emerged as potential options for early prognosis and customized treatments. Environmental factors significantly influence exacerbation prediction, emphasizing the importance of incorporating diverse datasets and environmental exposures. While challenges exist in acquiring data, interpreting models, and validating results, ML methods offer valuable tools to improve asthma treatment and enhance patient outcomes. Future advancements in the field will focus on integrating various data types, developing personalized predictive models, and improving model interpretability to integrate them into the clinical workflow seamlessly. By employing proactive, personalized, and data-driven management techniques that cater to each patient’s unique needs and align with population health goals, ML has the potential to revolutionize asthma care.